• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.282-289
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• 2008

Although there were some technological developments in improving the collaborative filtering, they have yet to fully reflect the actual relation of the items. In this paper, we propose the recommendation system using associative web document classification by word frequency and ${\alpha}$-cut to address the short comings of the collaborative filtering. The proposed method extracts words from web documents through the morpheme analysis and accumulates the weight of term frequency. It makes associative rules and applies the weight of term frequency to its confidence by using Apriori algorithm. And it calculates the similarity among the words using the hypergraph partition. Lastly, it classifies related web document by using ${\alpha}$-cut and calculates similarity by using adjusted cosine similarity. The results show that the proposed method significantly outperforms the existing methods.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.2
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• pp.60-69
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• 2013

Highlighted by recent security breaches including Google, Western Energy Company, and the Stuxnet infiltration of Iranian nuclear sites, Cyber warfare attacks pose a threat to national and global security. In particular, targeted attacks such as APT exploiting a high degree of stealthiness over a long period, has extended their victims from PCs and enterprise servers to government organizations and critical national infrastructure whereas the existing security measures exhibited limited capabilities in detecting and countermeasuring them. As a solution to fight against such attacks, we designed and implemented a security monitoring system, which shares security information and helps cooperative countermeasure. The proposed security monitoring system collects security event logs from heterogeneous security devices, analyses them, and visualizes the security status using 3D technology. The capability of the proposed system was evaluated and demonstrated throughly by deploying it under real network in a ISP for a week.

• The Journal of Korean Association of Computer Education
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• v.8 no.4
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• pp.15-24
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• 2005

The success of cooperative learning in small groups heavily depends on how the small groups are set up. When small groups are formed, the factors such as the objectives and characteristics of the work and the capabilities and interests of the group members should be considered to maximize the interaction among the group members. However, it is not easy for teachers to manually divide their class to small groups to satisfy such conditions. In this work, we developed and implemented a student grouping system that divides the class as appropriate as possible, when given multidimensional student data and a set of conditions for forming small groups. The grouping conditions can be heterogeneous, homogeneous, and both. The grouping system can easily be used by teachers since the system can be accessed by clicking a menu button embedded into Microsoft Excel. The system has also a wide range of application areas where object grouping by various conditions is needed.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.887-892
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• 2005

Under pure P2P environments, collaborative filtering must be provided with only a few service items by real time information without accumulated data. However, in case of collaborative filtering with only a few service items collected locally, quality of recommended service becomes low. Therefore, it is necessary to research a method to improve quality of recommended service by users' context information. But because a great volume of users' context information can be recognized in a moment, there can be a scalability problem and there are limitations in supporting differentiated services according to fields and items. In this paper, we solved the scalability problem by clustering context information Per each service field and classifying il per each user, using SOM. In addition, we could recommend proper services for users by measuring the context information of the users belonging to the similar classification to the service requester among classified data and then using collaborative filtering.

• Journal of the Korea Society of Computer and Information
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• v.25 no.5
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• pp.99-106
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• 2020

The scalability problem inherent in collaborative filtering-based recommender systems has been an issue in related studies during past decades. Clustering is a well-known technique for handling this problem, but has not been actively studied due to its low performance. This paper adopts a clustering method to overcome the scalability problem, inherent drawback of collaborative filtering systems. Furthermore, in order to handle performance degradation caused by applying clustering into collaborative filtering, we take two strategies into account. First, we use fuzzy clustering and secondly, we propose and apply a similarity estimation method based on user preference for movie genres. The proposed method of this study is evaluated through experiments and compared with several previous relevant methods in terms of major performance metrics. Experimental results show that the proposed demonstrated superior performance in prediction and rank accuracies and comparable performance to the best method in our experiments in recommendation accuracy.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.12
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• pp.66-72
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• 2011

In Japan, the specification for digital terrestrial television broadcasting systems has been approved for employing a transmission scheme for ISDB-T, which can accommodate HDTV service and portable mobile reception by adapting segmentation of frequency. In this paper, we have developed 3.5 inch mobile TV according to One-Seg system and presented the results of our development. In addition, we introduce two diversity schemes to compensate the defect that signal is interrupted when the portable TV is moving fast. In order to achieve a reliable communication in One-Seg system, we introduce STD(space time diversity) which uses more than 2 antennas at TX and cooperative reception diversity which utilizes signal-exchanging between different mobile terminals. And, its performance enhancements to reception reliability of previous system are evaluated by computer simulations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.9C
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• pp.896-905
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• 2009

In this paper, we propose an enhanced iterative ICI (Inter Carrier Interference) cancellation method for cooperative STBC-OFDM (Space Time Block Coded-Orthogonal Frequency Division Multiplexing) system. In cooperative STBC-OFDM system, ICI cancellation is necessary because ICI due to the independent local oscillators always exits. The conventional iterative ICI cancellation method has severe performance degradation due to the initial estimation error. Also the performance degradation increases as CFOs (Carrier Frequency Offsets) and modulation order increases. Therefore, in this paper, we propose an enhanced iterative ICI cancellation method which can reduce the initial estimation error by giving a priority to initial estimation and cancellation process. Through the complexity and performance comparisons, we verwey that the proposed method has better performance with approximately same complexity compared with the conventional method.

• Journal of the Korea Society of Computer and Information
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• v.11 no.3
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• pp.87-96
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• 2006

Most conventional database systems support specific queries that are concerned only with data that match a query qualification precisely. A cooperative query answering supports query analysis, query relaxation and provides approximate answers as well as exact answers. The key problem in the cooperative answering is how to provide an approximate functionality for alphanumeric as well as categorical queries. In this paper, we propose a metricized knowledge abstraction hierarchy that supports multi-level data abstraction hierarchy and distance metric among data values. In order to facilitate the query relaxation, a knowledge representation framework has been adopted, which accommodates semantic relationships or distance metrics to represent similarities among data values. The numeric domains also compatibly incorporated in the knowledge abstraction hierarchy by calculating the distance between target record and neighbor records.

• Journal of Technology Innovation
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• v.22 no.2
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• pp.51-75
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• 2014

Sustainability has already become one of the most important innovation policy agendas in many countries. Korea has tried to emulate this global trend in various forms, unfortunately with little success. A very strong tradition of centralized administration in Korea has meant that local communities and municipalities have remained more or less passive in tackling their own sustainability issues and problems. In recent years, with the impetus of huge democratization attempts in Korea since the late 1980s, there have been some local attempts to remedy these imbalances since the early 2000s. A series of local experiments to try and test green alternatives against the very hostile environment have been implemented, with varying degrees of success. This study analyzed cases of transition experiment to sustainable socio-technical system such as MUSIC project, Low Carbon Green Village and Citizens' Sunlight Power Stations. To draw politic meanings for sustainable socio-technical system, these content, process of conversion and feature were examined.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.2
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• pp.174-188
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• 2004

In the life environment changed with not only the quality and the price of the products but also the material abundance, it is the most crucial factor for the strategy of product sales to investigate consumer's sensibility and preference degree. In this perspective, it is necessary to design and merchandise the products in cope with each consumer's sensibility and needs as well as its functional aspects. In this paper, we propose the Fashion Design Recommender Agent System (FDRAS-pro) for textile design applying collaborative filtering personalization technique as one of the methods of material development centered on consumer's sensibility and preference. For a collaborative filtering system based on textile, Representative-Attribute Neighborhood is adopted to determine the number or neighbors that will be used for preferences estimation. Pearson's Correlation Coefficient is used to calculate similarity weights among users. We build a database founded on the sensibility adjectives to develop textile designs by extracting the representative sensibility adjectives from users' sensibility and preferences about textile designs. FDRAS-pro recommends textile designs to a customer who has a similar propensity about textile. To investigate the sensibility and emotion according to the effect of design factors, fertile designs were analyzed in terms of 9 design factors, such as, motif source, motif-background ratio, motif variation, motif interpretation, motif arrangement, motif articulation, hue contrast, value contrast, chroma contrast. Finally, we plan to conduct empirical applications to verify the adequacy and the validity of our system.